We have implemented two block ensemble methods. The first method is very intuitive which is the base Weighted Sum of the Blocks Output (Base-WSBO). The structure is depicted in Figure 2. The weight of the block output is a learnable parameter. The second method is the Squeeze-and-Excitation module [1]} to Weighted Sum of the Blocks Output (SE-WSBO). The structure is depicted in Figure REF . We can improve the quality of representations produced by the SE module[1]} by explicitly modeling the interdependencies between the output of all blocks. We allow the network to perform feature recalibration, through which it can learn to use global information to selectively emphasize informative block output and suppress unuseful ones.
